Artificial intelligence is likely to be a society- and business-altering technological development.

But, just like the advent of the internet before it, AI’s emergence will undoubtedly lead to as many empty promises from business leaders as it does actual use cases.

That’ll particularly be the case in the early innings of AI, which I believe we are currently in.

At any health care conference over the last five years or so, AI chatter was as ubiquitous as COVID-19 chatter was during the early days of the pandemic. But I’d often come away from that chatter with no more information on how providers planned to put AI to use than I had before.

This year is likely to be one of the first where a good chunk of providers are actually putting AI to use, however. That’s why, in Home Health Care News’ trends for 2024, we included the prediction that “providers will find ways to more seamlessly and strategically integrate AI.”

Over the last few months, I have tried to cut through the empty promises and ask providers directly: How are you currently using AI, or how do you foresee your organization using it in the near-term future?

More direct questioning, unsurprisingly, led to more direct answers.

In this week’s exclusive, members-only HHCN+ Update, I hope to take you behind the curtain on providers’ AI strategies across home-based care.

An AI prologue

First things first, every provider I talk to about AI generally starts off with a similar opening statement on AI, which is that they do not believe AI or other technology will be able to replace hands-on, human care.

Particularly in the early innings, that seems like the right mindset.

“We hear so much about tech in the home,” Visiting Nurse Health System CEO Dorothy Davis told me. “Not that I don’t think that’s an important piece, but I think the revolution is going to be on the consumer side and on the caregiver side. Tech is an enablement. If the user and the person impacted doesn’t understand it, the tech means nothing.”

That’s a key caveat. If AI cannot be implemented in a way that can be understood by a select few people – or in some cases a large group of people – then it is useless.

It’s also generally useless, particularly for generative AI, if there is not good data to feed into it.

Providers can’t go from an archaic operation with no data tracking capabilities to a future-facing, AI-embedded operation in one jump.

“I often describe data as the clay,” Guillaume Vergnolle, a senior data scientist at AlayaCare, told me on stage at Aging Media Network’s Continuum conference. “It’s your best material to come up with an [AI] solution. You need the right kind to come up with the solutions. So, when it comes to the retention problem, make sure that you’re actually collecting the right data to mirror what you’re trying to solve.”

AlayaCare is one of the home-based care vendors aiming to help providers out with AI. Its commitment to AI solutions – along with WellSky’s, for instance – is a heavy indication that providers will soon be further along with practical implementation.

Where AI will be useful

Compassus COO Laura Templeton told me that she sees AI becoming useful in two areas in the near-term future: documentation and scheduling.

“We currently have a couple of work streams right now — one being for clinicians — around how AI can make their job and role easier or better,” Templeton said. “We’ve been looking closely at how to consolidate and optimize processes by utilizing AI tools.”

Compassus leaders were the first to divulge AI use cases to me in December at the Continuum conference.

“We’ve piloted several scheduling programs where we’re using our clinicians at the top of their license, and where we are sending the right clinician, at the right time, to the right place,” Templeton continued. “Scheduling is one area that comes to mind where I’m excited to see what AI can do.”

Indeed, scheduling is one area where providers could use advanced help.

After all, staffing is a top concern for nearly all home-based care providers. Within that, most leaders will say the key issue they’re trying to solve is retention. Within that, scheduling is the No. 1 reason that home health workers turnover.

“We’re having humans doing things that humans don’t have to do, scheduling being one of those,” VitalCaring President Luke James told me. “Medical records. Systems work. Where can we apply some generative AI and some kind of workflow technology that can take most of it out of the hands of humans? Reacting to the exceptions only, for instance.”

Axle Health, a home health scheduling platform, announced a $4.2 million funding round Thursday.

James also mentioned documentation, which Jordan Holland – the VP of value-based contracting at Compassus – also dove into in December.

“Clinical documentation has always been a big one — which has a lot of different layered potential use cases,” he said at Continuum. “There’s the idea of talk-to-text, but then there’s also talk-to-text to other discrete fields. Talk-to-text is great, but is that actually going to help you facilitate filling out an OASIS form? There’s an added layer to that because that talk-to-text then gets submitted to another party.”

James added that VitalCaring “has to get more efficient in the back office with rates continuing to fall.”

That is the core driver of a lot of home health providers’ AI strategies: finding ways to become more efficient to avoid fallout from any rate cuts from the Centers for Medicare & Medicaid Services (CMS).

Alivia Care CEO Susan Ponder-Stansel is taking the same approach, but through a different lens.

A provider that has gone deep into value-based care over the last few years, Alivia Care wants to find ways to up reimbursement through better outcomes.

“We want to really be able to stratify risk and create a patient profile,” Ponder-Stansel told me. “There are certain algorithms that you can develop to say, ‘Okay, when these particular things happen, you need an extra visit, you probably need to do a med rec.’ Because all those things downstream help prevent that rehospitalization, help prevent that adverse outcome. So that’s what we’re looking at.”

Elara Caring CEO Scott Powers, meanwhile, told me that stripping caregivers and home health aides of non-value work is the “No. 1 use case” that he sees coming to fruition.

The home care side

Personal home care providers are generally approaching AI a bit differently, which is interesting to note.

For instance, Home Helpers sees it helping most on the marketing side, particularly for franchises.

“We use AI in our franchise-development process around identifying potential new franchisees, and doing some specific psychographic targeting,” Home Helpers President and CEO Emma Dickison said during a HHCN webinar last year. “Internally, for the team, where we see the biggest lift with AI … is in the marketing department. But there are just so many applications.”

Similarly, BrightStar Care isn’t writing big AI checks yet, but is, for now, using AI-enabled chats on its website to help out with back-office functions and to get feedback from clients.

But there also are rate concerns for these home care providers, some of which are similar to home health providers’ concerns.

For those that are diving further into Medicare Advantage (MA), for instance, there’s a need for more efficient processes to make MA beneficiaries worthwhile clients from a business sense.

“I think you just have to prioritize where you can make the biggest difference on the margins,” Kristen Duell, the EVP of experience and innovation at FirstLight Home Care, told me. “We need to create automation in certain areas, leveraging technology and leveraging machine learning so that we can reduce overhead costs – and sometimes field costs – so that we can take on those health plan [clients]. We need it to make economic sense.”

The post No Empty Promises: How Home-Based Care Providers Actually Plan To Use AI appeared first on Home Health Care News.

Sarah Khalid: I’m the product manager on the Labs team, so we focus more on the research and development, and the next suite of innovative features that are going to hit the platform, so typically AI, machine learning, these innovative next-generation technologies. We test a lot with those and get them into production for our clients.

Guillaume Vergnolle: I’m a senior data scientist in AlayaCare, so basically, I’m working on the implementation and how to create those AI models that can eventually help in some of the healthcare problems such as caregiver retention, or patient re-hospitalizations, or trying to extract important information from the clinical documentation among others.

Home Health Care News: Artificial intelligence has obviously been around for a while, but there’s been an explosion in terms of the popularity of it over the last few years. People are talking about it more, especially with ChatGPT and other language models. People are really wondering how it’s going to affect them, how it’s going to affect their business, how it’s going to affect their industry. I’m curious how you think it’s going to affect the whole health industry specifically.

Khalid: Yes, absolutely. Just to take it back and make this industry-specific, so we know that, of course, caregiver retention is such an issue within our industry. Just referencing Home Care Pulse’s recent reports: 64% turnover rates for our caregivers and our nurses in the field, 40% averages for back-office staff, including coordinators’ intake. This is a real problem that plagues our industry, of course.

Additionally, one in four caregivers are likely to leave within the first 30 days of being hired. What we’ve uncovered with some of the data that we’ve been looking into is that this is often due to scheduling-related issues, not being scheduled for preferred hours or preferred care within the first few months of working. There’s a lot of data and a lot of interesting insights that we can extract and pull out of our industry today.

When we think about how to combine some of that with AI and a lot of the generative models that are now being used and thought of, we can get really creative with some of the ways that we can combine those powerful technologies with our data. There’s a lot of creative different use cases. I think more and more, we’re going to start to see this being seamlessly integrated in the entire retention problem. I’m excited to talk more about how we also envision that.

Vergnolle: Yes. I guess we’ll be seeing AI being used more and more in many different industries, healthcare among others. It will also take a bit of time, because we know that AI models can be super good at very complex tasks, but sometimes can perform poorly on very simple tasks. We’ve seen the emergence of models like ChatGPT and others that are being better at tasks that they have never been trained on.

At the end of the day, the models will be as good as the data they’re being fed, so the data is really central in how you design your models. We’ve seen some very simple limitations. For instance, like models that can predict if nurses are about to quit their job. Then when applied to PSW, it will perform really poorly, just because it wouldn’t be looking at the right data points. We’re ramping up. We’re trying to find how to properly apply AI in this sector and all the steps that will lead us there.

HHCN: It sounds like a good place to start. It’s starting to collect data for businesses so they have that applicable data that can be fed to the AI model eventually.

Vergnolle: Definitely. Sometimes I often describe data as clay. It’s like your base material in order to build a solution that could be like a vase, for instance, if I keep on the metaphor of the clay. I guess one thing that you need is to get the right expertise in order to come up with those solutions. When it comes to retention problems, make sure that you’re actually collecting the right data to measure when your employee leaves, and for what reasons. To try to capture this data should really be the base when it comes to building solutions to try to figure out that.

Khalid: Just to add on to that, so now we’re starting to see that a lot of the ways that companies are storing their data, these databases are now being seen as knowledge bases that are eventually going to feed into the model. We can do a lot of interesting tasks on top of that knowledge base. Now, there’s the recent release of a lot of these Retrieval Augmented Generation models.

What we can do is we can ask a series of questions on top of that data and pull out some of these interesting ways that we can use this in combination with the current retention practices that we have. Absolutely the distress, the data is the most. It’s still the most important thing when we’re using AI as a tool to solve these problems.

HHCN: What are some of those retention use cases that you’ve seen so far?

Vergnolle: What comes to mind is identification first. That’s like how we can identify among the employees at your company who are dissatisfied, or who is about to leave. There are very simple ways to go through that. Surveying should be the first solution that comes to mind. Then we can try to think of how we can continuously measure this dissatisfaction across time, because surveys are just punctual, we can do them every couple of months eventually.

If we’re collecting the right data points, it can be like the number of hours that your employees have received. We can compare it against the number of hours in their contract or compare it to their availability. There’s a lot of data points that you can try to measure to make sure that your employees are having the service, like the volume of service they’re asking for.

HHCN: Yes, schedule volatility has been one of the biggest reasons. Owners and C-suite executives have told me that caregivers and home health aides are quitting. Particularly of late, even the best of the best workers have been burnt out because their schedule is so unpredictable from one week to the other, so it gives them no real assurance that their personal life isn’t going to be thrown a wrench week by week. I’m curious, what challenges do owners and C-suite executives face when they’re trying to implement AI that you guys have seen?

Khalid: The thing that comes across time and time again, as we touched on, is how the data is being collected, just to keep stressing on that point. Particularly when we’re tackling some of these retention problems, a lot of the time, just in the nature of this conference, Continuum, the data is being stored in so many different places. We actually see a lot of different owners that are storing their data, maybe in Google Docs, maybe just still in Excel, in a bunch of different sources. Still the challenge, not just in making sure that the data that’s being collected, is of quality.

Actually a lot of these large language models, they’re quite forgiving for some data quality issues if they have enough context. They’re forgiving for some spelling errors and some mistakes that traditionally have been really difficult to manage with more classical machine learning problems. Still, making sure that all of that data is harmonized, and as well making sure that when we’re speaking to the different stakeholders that are collecting and putting in the data, that everyone agrees that this is the central source of truth. When we’re talking about, for example, speeding into and predicting whether someone is satisfied, whether someone is at risk of churn.

If we’re collecting reasons such as termination reasons, a lot of the time these will be unstructured. They live in, again, a bunch of different places. We all need to come together and come up with more formal data contracts when we really start to work on these problems.

Vergnolle: Yes. I guess the two main challenges I’ve been facing on my journey as a senior data scientist is, one, the data pre-processing. It’s good to have the right data captured, but then to make the data speak, basically too. You need to curate your data to have it taking the right shape, and that takes most of your time. I was surprised because at the university, when you’re doing your master’s degree in artificial intelligence, you mostly work around models and AI, how to properly train them, how to run the evaluation. Then coming to the professional world, I was surprised how much time it takes pre-processing just to get the data in great shape. That’s one of them.

I guess the second other big challenge I’ve been facing is definitely getting a model into production because you need to validate, to ensure to have a pipeline in place, but also you need monitoring just to make sure your model keeps on spitting good results, that your results make sense. So many things can evolve. For instance, COVID-19 had a huge impact on all the models that were in place just because all of a sudden, all the data distribution has been changing. All of a sudden, new data points were coming in. We need to adapt. We need to measure how our models are performing before being deployed but also after. That’s also super important.

HHCN: Yes. If you have a thesis of where AI could help in your business and you go to implement it, what’s that process like over time? How do you make sure that once you implement it, it’s actually going to be able to be drawn out widely across the organization, and ultimately work and save you on your bottom line or whatever?

Vergnolle: One thing that really worked for us is being really close to the users. The end users, they will actually use the system that you’re trying to design, because in the end, they will be the one using it in their day-to-day to add it in their workflows. Adoption is very important. To have a good adoption rate, for that you need a good explainability and also trust. You need to build trust with your model.

If you can derive metrics, have a good evaluation metrics to be able to say, “My model works X percent of the time.” If you have access to historical data, you can run your model on historical data and see how if it was applied at that time, how it would have performed. That can help build trust. Then also with time, of course. You can give some trial periods where you can get used to the prediction and how to interact with your solutions. That can really help.

For the explainability parts, I’d say that most of the time AI is seen as a black box. Sometimes it is, to be honest. If we go back on the retention problem, it is not enough to say that an employee is at risk of leaving a job. You need also to be able to give the reasons why. That helps build that trust that eventually helps with the adoption. That’s, I would say, the highway to make sure that your product down the line actually helps the user in their workflows.

Khalid: Yes, just to stress that, of course, at the individual user level, it’s very important to build that trust. When we bubble it up to the agency level and the teams that we’re working with, it’s even more important to build that collective trust. Making sure that the workflows that we’re designing are not very foreign to their current practices, this is something that’s really key. It’s going to help a lot with adoption. That’s typically the method that we co-innovate with our users. I think that that’s probably the best method to make sure that these systems are being adopted well, and there is that trust being built in.

Then I think just having these conversations, again, at more of a collective level, because this is a tool, this is a technology that isn’t going anywhere. It’s going to continue to adapt and change rapidly. I think just having these open forum conversations about what other people are doing with AI and how they’re building these systems, these are all ways that we can start to build collective trust with the tool being used and being adopted.

HHCN: Yes. In regards to trust, I’ve heard, for instance, that sometimes like ChatGPT gives information to someone that shouldn’t have it or to make up answers. How do you grapple with those sorts of things that are still embedded in AI as we know it, if you’re implementing this into your business?

Khalid: Yes, it’s a real challenge. For us, I think, and for a lot of companies that are creating these AI systems, it’s still of utmost importance to stress that this is not meant to be a replacement tool, but it’s meant to be decision support. Throughout the entire process, when we’re training, when we’re building, when we’re thinking up how AI can be used, we are considering the process of the human being in the loop. Ensuring that we’re collecting the correct reasoning for how we’re able to deduct certain predictions, how we’re able to come up with certain responses, that’s of utmost importance.

I think that there’s a lot of fear generally when we talk about AI as it’s going to be replacing a lot of us humans and the tasks that we do day-to-day. The truth is, again, it’s meant to be more of a decision support tool. In addition to that, the industry that we’re in, the data is extremely sensitive. When we begin to experiment with a lot of these models, these large language models, even as simple as running an API call with, say, an openAI and playing with a ChatGPT. This is something that actually can result in data leaks, and breaches, and actually giving over your data to an external company.

I think we all need to also just be very informed when we’re experimenting with these models, some of the risks there when it comes to personal health information also being leaked. There’s a number of different things.

Vergnolle: I guess to add to that, it’s even more important that we’re in the healthcare sector. Down the line, all the predictions that you’re doing using those models could affect a patient’s health, so eventually life. Having the proper security layers are even more important in that sector. You need to adapt your strategies. For instance, all the proprietary models that are out there available, like the ChatGPTs and so on, most of them have been trained on web data. They’ve been scraping Wikipedia pages and many other pages.

You need to make sure that you can actually apply those models to the application domain that you’re working on. That may also require you to build your own guardrails. A guardrail is something that has been used by OpenAI and all those model providers to prevent the models to behave in a certain way. For instance, you can give it a try yourself. If you ask ChatGPT how to wire a car, it should say, “Hey, sorry, but I’m not allowed to answer that question.” In our fields, we also need to build our own guardrails. How do we prevent those large, like those models, those chatbots to not go in certain areas?

HHCN: If I am a Home Health Care executive and I’m considering AI and implementing it, how do you get started? Are certain organizations too small? What do you need in order to get started?

Vergnolle: I would say first, being data-driven is very centric. If you want to include any AI project, what is good is there’s a good amount of AI models out there if you need to use them directly. Though I would not start from which model to use. I would first encourage you to start to identify your problems and ask yourself which problem can be solved using AI.

I feel like often, AI is seen as a solution, whereas it should be seen as a tool. You can look for yourself. There are some articles talking about coffee machines augmented with AI like the first. That’s questionable, is AI properly used in that context. Whenever you’re starting a project, ask yourself the question, what would be the right tool to solve this problem? AI could be a part of the answer, but also make sure to consider which tool would be optimal.

HHCN: Oh, for instance, it can tell when a worker might be about to leave. The onus is still on the owner to make sure that they don’t leave. It’s part of the process, it’s not the entire thing.

Vergnolle: That’s it. For instance, if you don’t even have your hands on the data on what the turnover metrics are, maybe this is where I would start before actually envisioning adding AI on top of that.

HHCN: In terms of when you guys are working with home-based care providers, what are the biggest pain points that you’ve seen so far? What are some of the questions they have most of the time? Are there many themes that have come up?

Vergnolle: It’s one of the areas of challenges that we had, is generalization among others, just because each care, each agency or each provider, may have different ways, different workflows, and how to make solutions applicable to different markets. That’s one of the challenges that we’ve been facing. You need to make sure that you’re, in a way, flexible to allow for different markets to use your solutions, but also accurate in each of those. That’s one of the challenges that we’ve been facing.

Khalid: I’d also say, just to stress on Guillaume’s point of generalization, when we’re thinking about the different stakeholders that are going to be interacting with the systems, we really need to ensure that they are considered and thought of day zero. When we’re thinking about those processes, oftentimes, maybe when an owner is excited to just dive right into AI as a solution, as Guillaume stressed, it’s a tool. It is not necessarily going to be the answer for some of these problems. Being able to have that thorough setting up of what is the objective involving the stakeholders, because ultimately, what we’re trying to predict in the end is some of their reasoning processes for how they come to the conclusion that, actually, this person is at risk of churn.

A lot of these models now too, in the frameworks that have been released, which are really interesting, is you can begin to see how the model is reasoning in each and every step, and how it’s using the data, how it’s interacting with a set of different APIs, and how it’s coming up with the conclusion that this is why I believe that this nurse is at risk of churn.

We still need the stakeholders throughout that entire validation process to make sure that we have that trust. I would say that, yes, for owners, not getting too excited about how we can start to use LLMs right now, and maybe thinking about it in a more holistic perspective of that.

HHCN: I also imagine the owner bringing in a caregiver or a home health aid may be beneficial because you have their perspective as you’re building the model, and you’re understanding why they might churn.

Khalid: Completely, yes. Another use case that actually feeds into some of those problems with churn is when caregivers and nurses are out on the fields, often with some of their day-to-day tasks, they’re doing a lot of this documentation. They’re doing a lot of note-leaving and note-taking. We can actually use another set of LLMs and machine learning to pick up on different patterns when they are leaving some of those notes, pick up on different sets of behaviors.

Maybe we can pick up on sentiment when they are leaving those notes as well. That gives us a clue, too into how they’re feeling and how that feeds into retention as well. That’s an indirect way to understand their overall sentiment. Doing direct interviews, that is always something that’s completely valuable, and something that we need to consider when we’re building these systems.

HHCN: Is there anything that you’re excited about that could be applied in the future that maybe not be possible now, whether it’s in the home health industry or just in business at large?

Vergnolle: I’m personally very excited. We’ve seen those models coming up that are mostly around chats. We’re seeing more and more models coming out that use multi-modalities, so not only text, but also pictures, videos, sound even. Can you imagine having almost an assistant that could pull you for the whole day where you can monitor your patient’s health like vitals, for instance.

Even with pictures, you could look at a wound. Say, if it has evolved, if it’s actually getting better. As a caregiver, for instance, just having that device and just saying out loud all the care documentation that you want to take. At the end of the day, it just makes you a nice paragraph that is just a summary of what you’ve been sharing doing that day. I’m seeing a lot of applications that mix all those different applications and modalities. In the future, they could build great products.

Khalid: I‘m actually quite excited about a lot of the creativity that we can evoke with these models. You’re starting to see a lot of different creative use cases that, yes, we can implement into our businesses. Just from, again, more of an outside perspective, so I personally love to write. I actually like to evoke large language models in more of a Socratic dialogue to get deeper into understanding some different holes of creativity. I think that that’s something that’s really interesting that we could also in the future implement with some dialogue with some of these agents.

Right now, it’s an interesting time because there’s a lot of focus on the knowledge that is available on artificial intelligence. I do think in the future, businesses, society at large is going to start to adapt to more artificial wisdom, in a way. How can we ensure that the systems that we’re building out are in sound principles? We’re not just focused on this age of information and getting overwhelmed there.

I think that just due to the nature of where we are right now in the industry, the ML industry, the home care industry, we’re going to start to think about these more perennial, I think, larger existential questions, and build our systems in a way that it’s touching on these principles very, very comprehensively.

To learn more about how AlayaCare can help your organization ensure operations are consistent across multiple locations with real-time information updates for key stakeholders, visit https://www.alayacare.com/.

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Home-based care providers have been waiting for the other shoe to drop on dealmaking for a year now. As 2024 nears, that shoe may be finally landing.

And, in addition to M&A, genuine AI application in both home health care and home care appears to be right around the corner.

Those were two of my top takeaways from Aging Media Network’s Continuum conference this past week.

Senior care providers of all shapes and sizes gathered together Thursday, with topics covered including: dealmaking; AI; recruiting and retention; interoperability; new care models; better outcomes; payer-provider relationships; and much more.

In what seems to be a rare occurrence across industries, provider leaders actually explained how AI would be implemented at their organizations in the near-term future. Plus, venture capitalists and private equity players discussed what they’re looking for over the next 12 months.

AI will affect every industry in the long term. A dealmaking bump – particularly with PE getting off the sidelines – will affect the home-based care space in the short term, though.

I dive into those two topics and their importance in this week’s exclusive, members-only HHCN+ Update.

PE, you’re in

Compared to 2021, every home-based care dealmaking year looks slow. But 2023 has been particularly slow, and that’s largely because of PE firms’ unwillingness to get back in the game.

Over the last year, we’ve been hearing that – eventually – PE firms will kick their activity back into full gear. That has not been the case.

But even if interest rates go up once more, or stay where they’re at, PE firms will have to begin deploying capital at a greater rate very soon.

“Private equity is sitting on $900 billion that they need to spend,” Dexter Braff, the president of the M&A firm The Braff Group, said at Continuum. “That is a big problem for them. It’s a good problem, to some degree, but it’s use it or lose it. If they don’t invest that money, they have to return it.”

Through the first three quarters of 2023, PE deals in health care services were at their lowest point since 2017.

The third quarter only saw 25 home health, hospice and home care deals, according to The Braff Group’s data.

“We’re in a mixed bag as we go into 2024,” Braff said. “Overall, I think the markets are going to improve, mostly because PE has been on the sideline not for a year now, but 18 months. And that’s not their business; they have to buy. They have to. They may be kicking and screaming, but they will, and it will push the market back up.”

Valuations for home health, hospice and home care companies have also come down since the buying craze that defined 2021.

PE firms are wary because of the macroeconomic market, but, as Braff pointed out, valuations dipping means they could pay similar prices now for likesized sellers they bought in 2021.

He also pointed out that the demand for quality home-based care entities has never gone down, even over the past two years.

The New York City-based InTandem Capital is already heavily invested in home-based care – in HouseWorks, in Pediatric Home Service and in Providence Care.

But Brad Coppens, a senior partner there, further delved into what the firm would be looking for in the future.

“We have, quite frankly, long been disinterested in investing in the alphabet soup of post-acute and siloed approaches. That’s sort of a 1.0 model,” he said at Continuum. “Where we think the interesting thing to do is to evolve into a 2.0 model where you look at care coordination, care navigation, the closing of care gaps – that sort of thing.”

Coppens doesn’t speak for the entire PE community, but many providers have already begun to build out the entire continuum, so to speak, within the home. For instance, providers will have home care, home health and palliative care. In some instances, they will even go further, investing in higher-acuity models such as SNF at home and hospital at home.

AI utilization

AI coming to home-based care is not an interesting inevitability.

What’s more interesting, instead, is what use cases there will be for it.

“We can get really creative with some of the ways that we can combine these powerful technologies with our data,” Sarah Khalid, a data and intelligence product manager at AlayaCare, said at Continuum. “There’s a lot of creative and different use cases. I think, more and more, we’re going to start to see this being seamlessly integrated in the entire retention problem.”

Documentation is an area where some of the largest providers are beginning to implement AI.

But, if implemented correctly, retention seems to be the area where home-based care providers will benefit the most from AI – and in short order.

Volatile scheduling is the No. 1 reason why home health workers leave their jobs. Unpacking that problem can be a herculean task, however.

That’s, ideally, where AI comes in.

“It’s that scheduling piece,” Compassus COO Laura Templeton said at Continuum. “We’ve piloted several scheduling programs, where we are sending the right clinician, at the right time, to the right place. So, scheduling is one I can think of, for sure.”

Elsewhere in retention, AI models can help predict when an employee may be about to leave.

But that model can only be built for a home-based care company if they are tracking the correct data. For instance, the model needs to learn why people are leaving in the first place – through exit interviews – and when exactly they have left in the past.

Those may seem simple things to track, but having reliable data points in those areas can take years to compile.

“I often describe data as the clay,” Guillaume Vergnolle, a senior data scientist at AlayaCare, said at Continuum. “It’s your best material to come up with an [AI] solution. You need the right kind to come up with the solutions. So, when it comes to the retention problem, make sure that you’re actually collecting the right data to mirror what you’re trying to solve.”

The post Private Equity, AI Likely To Power Home-Based Care in 2024 appeared first on Home Health Care News.

Staffing is a universal pain point for home health providers.

Specifically, that pain point is about recruiting and retaining talent, and most provider leaders argue that retention is the area their organizations need to focus on. Break retention down even further, though, and you find a main culprit for turnover: scheduling.

“[Scheduling] is one of the things that’s most inefficient in our business today,” Chris Gerard – then the CEO of Amedisys Inc. (Nasdaq: AMED), and now the CEO of TheKey – told me last year.

A study conducted by University of Pennsylvania’s Population Aging Research Center in 2021 found that scheduling volatility was a reliable predictor for turnover in home health care.

Volatility was measured by the the variability in daily home health visits over a month period, crossed with the likelihood of a nurse quitting. Nurses with low schedule volatility were 40% less likely to quit than the average home health nurse, while nurses with the most volatile schedules were 50% more likely to quit than the average.

Yet providers are still grappling with the issue of schedule volatility, and find it to be a massive logistical hurdle.

It also exists on top of other and larger concerns, such as shrinking margins due to fee-for-service rate cuts and taking on more Medicare Advantage (MA) patients. Scheduling can get lost in the shuffle.

But an argument can be made that scheduling should be placed at the forefront, as its ties to retention are so strong that providers can no longer ignore it amid those rate cuts.

That’s the topic of this week’s exclusive, members-only HHCN+ Update.

The scheduling problem

Most home health provider leaders recognize scheduling as an internal problem. But they don’t always have the time, or the solution, to do anything about it.

Last year, HHCN and the home-based care technology company AlayaCare conducted a survey that examined the home health industry’s biggest pain points. Close to 20% of respondents said inconsistent schedules were their agency’s top reason for turnover.

More providers are starting to believe that scheduling should top that list. AlayaCare is also one of the technology companies that’s trying to improve its clients’ scheduling processes.

“Schedule volatility – that’s a big thing that we’ve really been working on. When a nurse or therapist has one visit one day and seven visits the next day, you can’t build your life around that,” The LTM Group CEO David Kerns told me last week. “You’re missing your kids’ soccer games, patients are getting frustrated because you’re showing up late and then here you are another day and you’re only doing one visit.”

Based in Dayton, Ohio, The LTM Group is a network of home health, hospice and home care agencies across the Midwest. Kerns called retention one of the provider’s “biggest costs, and biggest areas to improve” in the near-term future.

Behind that problem is – at least in part – scheduling. That’s why Tina Hardwick, the president and co-founder of LTM Group, has begun to dig further into it on behalf of her company.

“It’s something we’re looking at across the organization,” Kerns continued. “How much is that individual clinician’s schedule varying? Then we’re looking at it between clinicians. If you have one clinician who has 10 visits scheduled and another one who has 30 visits scheduled, that’s going to create that person — who probably is your most productive employee — to be upset and leave. Fixing that within their own schedules and within the team schedule, that’s a huge focus right now.”

Traditionally, scheduling has been conducted manually across home health care. Last year, Amedisys had over 700 manual schedulers employed across its over 500 locations.

That isn’t always a sound process. Gerard even said, within Amedisys, those 700 schedulers were all conducting business “a bit differently.”

Shelley Ackerman, a home health veteran and physical therapist by trade, witnessed this problem firsthand when she was on the provider side of the industry. She’s since left to try to solve it as co-founder and COO of CareStitch, a home health scheduling software company. 

I asked her earlier this year what was wrong with providers’ current scheduling processes.

“Well, it’s not always clear what their processes are,” she said. “A lot of times, especially the larger agencies, when they have multiple schedulers, they’re kind of working in silos. You have to define their processes and make them more efficient, removing that manual redundancy that the schedulers are having to do today.”

Gerard himself proposed technology-enabled solutions, ones that would centralize that process. It’s likely that Amedisys has taken steps to do that since last year, but they’re also in the midst of being acquired by UnitedHealth Group’s (NYSE: UNH) Optum.

If a provider as large and advanced as Amedisys has had recent troubles with scheduling, it’s a near-guarantee that most of the 10,000-plus other home health agencies across the country are having trouble, too.

It can also trickle down to patient care. The referral rejection rate is higher than ever, for one, which is reducing patients’ access to care. That’s mostly due to staffing inadequacy, which can be improved by less turnover.

On a micro level, a clinician with 10 visits coordinated in a sloppy way may have a difficult time getting to that 9th or 10th patient of the day.

“You can order a pizza right now and you know exactly when that pizza is going to be here and who’s delivering it,” Kerns said. “Yet in home care, where it’s dependent on your health and going back to the hospital, you don’t know when they’re going to be here, you’re waiting on a phone call, you don’t know who’s showing up. There’s really a disconnect in our industry and really an opportunity to improve.”

In a vacuum, scheduling in home health care is already a problem – it’s tends to be unorganized and inefficient.

It also has a downstream impact. It affects retention greatly, which can lead to increased costs and suppressed growth. It affects clinicians, and, in the end, patients.

The post For Home Health Providers, Clinician Scheduling Stands Out As Turnover Culprit appeared first on Home Health Care News.

Chase Potter, the vice president of professional services at AlayaCare, has been named a 2023 Future Leader by Home Health Care News.

To become a Future Leader, an individual is nominated by their peers. The candidate must be a high-performing employee who is 40-years-old or younger, a passionate worker who knows how to put vision into action, and an advocate for seniors, and the committed professionals who ensure their well-being.

Potter sat down with HHCN to talk about how the home-based care space can save health care systems in the near-term future; why providers and vendors should not underestimate caregivers’ abilities; and much more.

HHCN: What drew you to this industry?

Potter: Many folks have a deep initial connection that drew them into home care. For me, that that came a bit later on.

I was a consultant at IBM, and struggling to find real meaning in that space. I stumbled upon a post on a ball hockey message board about an opening for a role at a new tech company focused on on home care, and it piqued my attention.

I met the founders and I dug more into the industry content. It became clear to me there was this huge opportunity for technology to partner with providers to solve some of the biggest problems our society is facing and will face. So I took the leap, and I joined joined AlayaCare.

Then, a few years later, it really hit home for me. My father had what was thought to be frontal temporal dementia, and his ability to care for himself gradually deteriorated until he needed to move into a long-term care home. And every time I visited him, I witnessed the relationships his caregivers had formed with him, and how they built a meaningful life for him in that home. That really hits deeply with me, around the importance and the impact that providers can have on society and individuals.

What’s your biggest lesson learned since starting to work in home-based care?

I’ve learned a lot. But I would like come back to one thing. A colleague of mine likes to say, ‘If you’re not caring for the person in the home, how are you supporting the people who are?’

That is something that continues to resonate strongly with me. We’re not the ones in the homes, but everything we can do to facilitate better care for clients – whether it’s making it easier for a scheduler to match a client with the right caregiver, or ensuring that there is the ability for real-time communication between care teams – that all makes a difference in the level of care that a client receives them at home. It makes a difference to the experience of the caregiver.

If you could change one thing with an eye toward the future of home-based care, what would it be?

I’ve implemented a lot of technology in home care over the years. And one thing I’d like to call out is that we shouldn’t be underestimating caregivers and clinicians and their capacity for change and innovation.

We are often really quick to throw away a big change as impossible and too big a change management risk. Caregivers and clinicians adapt to challenges in the home every single day. And it’s a mistake to immediately discount their ability to embrace change at a different level.

I’ve seen 80-year-old caregivers pick up smartphones for the first time, and upon realizing why this change will make a difference positively in their lives, adapt. Let’s not underestimate the folks that are in the homes and their ability and capacity to change and innovate.

What do you foresee as being different about the home-based care space looking ahead?

Technology as an enabler for home care providers.

When I entered the the industry, that enablement was really focused on digitization, and how we can get off of paper – technology just as a mechanism for replacing what was done on paper.

But, in 2023 and beyond, the focus is shifting towards how technology can empower staff to form deeper and more meaningful connections with caregivers and clients. At AlayaCare, we’ve been talking a lot about this war on repetitive tasks. What are things that technology can do to reduce the administrative burden and allow coordinators and caregivers to focus more on the deeper personal connections?

In a word, how would you describe the future of home-based care?

Revolutionized. That’s maybe a really weird thing to say about a traditionally slow-moving industry. But the time is right.

We’ve exited the pandemic with fragile health care systems all around the world. And one piece that has come through clearly is, not only do people want to age in the place that they call home, but we need to be able to support that or these fragile systems are going to crumble. That sounds really bleak. But it’s not.

We’re seeing AI beginning to hit primetime. We’re seeing a focus on caregiving as a career and elevating it as a profession. Larger scale systems are beginning to understand the impact that home-based care has on the entire health care system. I don’t think it’s too much of a stretch to say, as we look at the future, there’s an opportunity for a revolutionized system.

What quality must all Future Leaders possess?

The need for empathy. Being able to understand the different stakeholders that live in this industry, whether that’s being able to put yourself into the position of the caregiver, the coordinator or the client when you’re thinking about decisions that impact your business and the industry. Leading with empathy, and not just looking at the dollars and cents.

To learn more about the Future Leaders program, visit https://futureleaders.agingmedia.com/.

The post Future Leader: Chase Potter, Vice President Of Professional Services, AlayaCare appeared first on Home Health Care News.

Home Health Care News: I think there’s a lot of exciting technology and we’re going to talk through a little bit of what’s real, what’s not real, what’s out there right now, and what’s coming in the future.

A lot of what we’ll discuss relates to workforce shortages and payment challenges, which are driving technology innovation. Naomi, I want to ask you at a very high level what do you see as the two or three game-changing types of technology in home-based care right now?

Naomi Goldapple: We can’t ignore LLMs, which are Large Language Models which is what’s running ChatGPT, but I don’t mean ChatGPT — like the one that everybody uses to cheat in school or write recommendation letters or translate — but the actual underlying models that you can use for all kinds of different applications. It’s really quite exciting and revolutionary right now. I can tell you that with my team, there are things that we’ve been building for the last year and a half using natural language processing [NLP] that we literally had to trash because these new models are just so much better. There’s so much that you can do with it.

It’s very exciting. I also think the world of wearables is becoming more and more of a reality, so it’s less gimmicky than it was before. Like these little sensors in the home for fall detection or strap something onto your grandmother and then you can detect things in that. Those were a little bit invasive and I’d say gimmicky and now they’re becoming a lot less invasive.

It’s going to be a game changer when we don’t have enough caregivers to be physically in the home. Then my last one that I’ve always been a big proponent of is voice. I think being able to use the Alexas, the Google Minis, and all that, but with the large language models. Instead of writing your prompts, these can be questions and it works just as well. There’s a lot that you can do for remote patient monitoring and for drug adherence, and there’s all kinds of stuff that you can do with this technology.

These are areas that have practical uses right now. What about long term? What’s coming next?

What I think is coming next is actually these things becoming mainstream and becoming part of processes and becoming regular technology that’s actually embedded into processes. Right now, it’s a little bit gimmicky. You could be using ChatGPT maybe to help a little bit here and there but how do you actually leverage this so we’re squeezing out costs from the processes, and we are really making things more efficient. I think we’re going to see in the next two to three years real differences in efficiencies because honestly, there’s no choice. Everyone’s got to cut costs out.

The long term might not be as sexy but is really taking these sexy things and making them reality.

We’re going to talk a little bit more extensively about AI right now. How could predictive AI in particular revolutionize data utilization for home-based care agencies allowing them to make better decisions than they currently do?

That’s an easy one because predictive AI is able to use predictive algorithms, and there’s so many different things you can predict. There are companies who are making real gains with, for example, claims or things like, do I want to predict whether I should take on this referral? Is it going to be profitable? Do I have the staff to staff this? Those types of things are really very important in terms of keeping the profitability up.

There’s also predicting risk, risk of so many things. We’re in a caregiver shortage, so risk of who’s at risk of leaving. That’s pretty important, and you need to know early who’s going to be leaving so that you can actually mitigate these. Getting those early warning signs. Something that I know I’ve been working on a lot is the risk of hospitalization. Risk of any of these adverse events.

Being able to automatically consume all of the data points that are collected every single visit, and being able to automatically put people into different categories, high risk, low risk, medium risk, and then what do I do about that, so that I can meaningfully move the needle with the measurements.

Sometimes from my perspective, I’m not an expert when it comes to AI, but it seems like within the past six months there’s been so much hype about ChatGPT. How much of the power of AI is overblown? What could it really do? Is it really game-changing like we said earlier?

Yes, I think it is, but not in the way that I think everybody got super excited about three, four months ago because all of a sudden everybody was able to just go on to the internet and ask their own questions and see the marvel of this agent being able to spew back amazing stuff. It really quickly democratized AI. It wasn’t just a bunch of researchers who were able to see the power of these models, it was everybody. That got everybody really excited, but it has died down now because aside from writing a letter for you, doing some translations, maybe writing a blog post, how can I actually weave this into my processes?

That’s where it’s going to change. There’s some things that people are using right now, for example, Copilot. GitHub Copilot is what ChatGPT is for text, it is for programmers. It’s pretty amazing. You can say, this is what I want to do, I want to build an application that does X, you can start it off and it can literally write all your code for you. Right now people who are building applications, we’re seeing like 30%, 50%, 60% productivity gains in terms of being able to be more efficient. Those things are pretty amazing. Where we can use the large language models is things like summarization. Going through the nurse’s notes, going through all this and picking out what’s important, and just summarizing that back.

Being able to do the question and answer, tell me about this patient, tell me about this client. Really being able to query any of your datasets with natural language is a game changer. Think of even accounting. I want to know if this particular visit is going to be profitable. It can go into your dataset and give you back a point of view — but again, we always have to be careful that there’s a human involved because it sounds smart but it’s not really smart. We know it’s intelligent, but it’s really still mathematical. Everything is just a prediction and it’s predicting the next word, or it’s predicting the next thing based on what it’s been taught.

It’s not really using outside intelligence to make that. You need to have a human that’s working alongside.

You mentioned using AI as a means to predict caregiver turnover. Can you talk a little bit more about that? How’s that being done exactly?

Sure. We collect information about our caregivers all the time. We know what their schedules are, we know what their skills are, we know what their availability is, we know what their behaviors are. Do they usually clock in and clock out with accuracy? How long do they have to travel during the day? We have all kinds of information and we can start to see patterns in that information.

Through the research that we’ve done on my team, we can see a few things. One is the higher their utilization is, so whatever the delta is between what are the hours they want to work and what are the hours that we’re giving them, that is almost the number one driver of happiness or satisfaction for caregivers. If they can have a schedule that is allowing them to earn a fair wage and get the hours that they need, they’re going to be pretty happy.

That’s very important as a metric to keep an eye on, to make sure that that delta doesn’t get too big. We’ve also seen things like: what is the delay between hire and actual first visit. If that is too long, they get disenchanted and they leave right away. You have to make sure they have the white glove treatment and what’s happening in the first 30 days, 60 days, 90 days, make sure they’re part of the family, and that works well.

You can have that on a dashboard, you can have metrics and you can be able to pinpoint when something is going off, and you can literally pick up the phone and be, “Hey, what’s going on? Do you need some more hours?” You can really mitigate this. We’ve been looking at trying to understand the groups, the clusters of caregivers by their behaviors. We can see there’s certain types that clock in and clock out with a lot of accuracy.

Some of them work, they do their documentation right away. When we do this, we have clusters of, we call them the hard workers and we can see hard workers have these types of characteristics. Then we had some that came out as sloppy workers and they had different characteristics. The highest numbers of clients who block them, they usually try to clock in when they’re not physically there yet, or a bunch of other things. You can start to identify behaviors and say, “This one smells like that type, I want them to be more this type, how can I judge their behaviors?” Be really data-driven about it.

You’ve already mentioned some really amazing real-world use cases of AI, and there’s probably several more that you could pull from, but do you have two or three real-world examples of providers doing something cool with AI that you haven’t talked about yet?

Well, we definitely see a lot in terms of claims processing, so reducing the error rates in claims. That’s where we can use something called anomaly detection, where we can see what a clean claim looks like, and then if there’s anomalous behavior that can be picked out before things are submitted so that they’re not rejected.

There’s definitely been a lot of impressive reduction in numbers of rejected claims by using anomaly detection algorithms. Then I think I’ve seen more and more what I was talking about in terms of caregiver churn. Then because the industry is forced to be more metrics-driven in terms of outcomes and reimbursement based on outcomes, we’ve seen a lot using these algorithms to get better total performance scores and try and reduce those hospitalizations, those falls so that they can get better overall metrics and really protect their reimbursements.

What about future uses? Are there any use cases for AI moving forward that you find really exciting but we’re just not quite there yet?

I alluded to things in the home, and I think this is really where we’re going. There’s not enough people to be in the home all the time. People want to be in the home, and there’s technology that’s becoming more and more accessible to be able to help monitor in the home, and even be interactive with the loved one at home to be able to make sure, are they taking their medications?

Is there anomalous behavior today? Usually, they wake up around this time and then they make it to the kitchen around this time, and then they do it this time. These sensors can try to see, they don’t seem to be getting up within the same timeframe today. These alerts can go to a caregiver or to other people to say, “You know what, they might be at risk of a fall. Something might have happened,” and you can go in and mitigate.

I think this ability to do remote patient monitoring is getting a lot more sophisticated and can be even interactive. Even things like loneliness, where they can start to talk to these agents who can talk back about, I’d like to hear this song, what’s the weather today? There’s a storm coming, and they’re afraid of storms so there can be more interactivity that can really be leveraged so that the caregiver doesn’t have to be there 24/7, and that can really help. I’m pretty excited about that.

In one of your previous responses you mentioned how important it is for there to be a person behind the AI tool, what are some of the other dangers of maybe leaning too strongly into AI?

I don’t know if you’ve heard the term hallucinations when all this came out three, four months ago and everybody’s playing with ChatGPT, but then there was also Microsoft Bing that came out at the same time, and then people were having conversations with Bing and all of a sudden Bing went off into a strange tangent and was telling this guy that he should leave his wife.

I was like, what is going on around here because these models, they tend to, if you don’t put guardrails, they can hallucinate. They start to grab information and contextualize in ways that are going off of what you want it to be doing. You have to make sure that they’re designed properly, but we always have to make sure that what we’re building is just decision support.

It’s not prescriptive, it’s not replacing because it really is the professional that will make that call. There’s some funny examples. If you are a caregiver, let’s say, instead of going to a visit as a caregiver, you want to just say, “Hey, can you tell me what has changed since the last time I visited this client?” Maybe it was the week before.

How nice it would be if it could just summarize for you, “Well, from the last time you were there, they changed this medication, they fell once, this and that.” That would be so great instead of hunting and pecking in your application or even in paper to try and read what the last person wrote, that would be great. When we were playing with this, the first thing that comes back is just the basic demographics about, well, this person is a 90-year old woman with these comorbidities, etc. so you just get a little summarization.

Then there’s what’s changed since the last one. One of them we saw was this person, let’s say Mr. X is a newborn. We were like a newborn, why would it be a newborn? We realized that the date just took today’s date, that date was blank. It was today’s date and therefore the large language model just assumed, well, they’re born today, therefore it’s a newborn. We were like, how do you have a newborn with all those comorbidities? You really need a human to take a look at that and make sure that you correct those types of things and that you train it properly.

One of the dangers is definitely the data. You have to make sure that the data is correct and accurate. You also have to put on those guardrails because you also want to make sure you’re not sending a bunch of personal health information to OpenAI, which you could very easily because when you’re playing with ChatGPT, they’re using your questions and your data to make it better.

You don’t want to be doing that with information in your database. You need to put guardrails. The other thing is about privacy as well. You have to make sure that the data that you want to protect is being protected and you can just share cleansed data, anonymized data, and you’ll still get the information out.

We spent a good chunk of this conversation so far looking at AI specifically. I want to shift gears and talk about data and data strategies and mistakes that providers typically make when it comes to their data strategies. What are some of the common challenges that home-based care agencies face when it comes to effectively using data?

I’m sure nobody would disagree with me, data capture, data input, and consistency. Getting everybody in your organization to input data in a timely and accurate fashion of course, it helps when you have fields that will validate, but everything starts with how you’re capturing the data. Garbage in, garbage out if it’s a sophisticated algorithm or if it’s just for regular information, that’s pretty important.

I’m finding that over the past three years, I feel like I would be talking to maybe one data person at providers, and now I feel like I’m talking to data teams. The providers are getting more sophisticated and really starting to leverage the data more, and really understanding where your data is coming from. One thing that we do notice is things like schema changes.

If you are relying on certain data to then amalgamate for other downstream processes, so let’s say you’re taking data from one vendor system, and then you have data from another vendor system, and then you’re making a report, and then that gets sent on to X and maybe that’s your utilization report that people are depending on, you have to make sure that if those vendors or if somebody changes anything in the database, that you’re aware of that so that you change all your downstream processes so that everything doesn’t get broken.

Everything is becoming very amalgamated because you want to get all the aggregated data together so you can get the fullest picture. You have to make sure that those data contracts with wherever the data is coming from, that those are set in place.

You just mentioned a few really good ones, but are there any other best practices providers should keep in mind as part of their data strategies to make sure that they’re collecting data that they could then actually act on?

One thing that I always talk about is being very hypothesis-driven. Why are you collecting this data? Why are you putting together this report?

These things are really important, and what are you going to do with it, because I’m sure you’ve created, or you’ve looked at dashboards that they were interesting at the beginning and then you stop looking at them, or you look and you’re like, oh yes, but what do you actually do with those results? Especially in AI, you have to think about how people are going to consume these predictions, because in AI everything comes down to a number.

It’s a prediction. It’s like 0.67 and you have to convert that into something that is really actionable. Maybe 0.67 means a medium level of risk, but it’s rising. If I’m telling that to a caregiver or a clinical supervisor that this particular patient was stable and now something is changing, what do I do about that? We don’t want to be too prescriptive again because we don’t want to say, “You should do this, because we don’t want to be responsible for that.

You still need the professional to make that call, but they can come up with all their mitigation strategies. When it’s somebody who’s medium risk with these types of things that are at a risk of fall and are not taking their medications, go to our guides, and this is what you have to do. You have to make sure that everything is actionable. If you see that there’s a caregiver that is at risk of churning, what do you do about it?

Do you just say, “Huh, it’s too bad, they’re probably going to quit next week. What do I do about it?” You need to make sure you finish the workflow and you think these all out and that you actually pull out the proper data that’s going to answer those questions.

During a lot of these conversations, I love going back to real-world examples to paint a picture of the things we’re talking about. When it comes to successful and effective data strategies, could you maybe share a real-world example or two of providers doing data-driven decision-making effectively that has had an actual positive change on their business?

One of the things that my team was working on for the past few years is all about schedule optimization. Using optimization algorithms to make sure that we’re not leaving big holes in schedules, that the right person is at the right place at the right time with the right skills, and continuity of care. You make that all configurable. What has been designed and what we’ve seen now is that a scheduler comes in the morning and says, “What are all the vacant visits that I have to fill?” They basically press a button, say optimize, and boom, it gives you all the proper matches because you’ve configured it properly.

We’ve seen some providers, they said this is something that used to take us the entire morning or even into the afternoon trying to find who’s the right person. With the press of a button, you have everything done in about 10 minutes. Sometimes you send them, you put it onto the schedule. Sometimes you have to do a shift offer, depending how you’ve organized things with the care workers.

This has eliminated tons and tons of repetitive tasks. Now, this only works if the availability is up to date, if we actually know when people are available. It only works if the skills are up to date. It works if we have the care worker and the client’s home addresses up to date. If any of those are wrong, it’s going to give erroneous answers. Then the scheduler is not going to trust it. Because you get very, very little margin for the user to actually trust these algorithms.

Because they’re already mistrustful, they think they’re black boxes. You want to try to make these as explainable as possible, given these are the reasons why it came up with this, and these are the actions. You have to design all that workflow. This is something we’ve seen has really started to change dramatically in terms of taking something that would take a morning, an entire day into a few minutes, which is pretty exciting.

There are other parts of healthcare that seem further along in their data journeys, so to speak. When we think about home care, how sophisticated is the industry at this point in your view?

If we compare it to the hospital system or other healthcare. Back in 2016, Geoffrey Hinton, one of the godfathers of deep learning. In 2016, he said, nobody should study. We are not going to need any radiologists in five years, that entire profession is going to be wiped out because AI can read the X-rays much better and much more accurately than any human.

We’re now in 2023, 5 years have gone by and radiologists are still needed. Today, we’re starting to see hospitals really starting to use this technology to actually not necessarily reduce the number of radiologists, but to reduce their workload. They only get now, here are the 10 that are potential tumors, let’s say, versus going through 500 and going through.

Now these are starting to be not just point solutions, but these are starting to be systematic in the healthcare system. I don’t think we’re there yet in home care. In home care, it’s still a lot of playing around and point solutions. I think over the next few years it’s going to become systemic that these applications are going to be part of the regular workflow because we need to get more efficient.

I want to get to some of the other emerging technologies that you’re really excited about, but tie a bow in the data point specifically, and AI. What have been some of the macro trends that are advancing the use of AI and advancing data strategies in home care? For me, for example, value-based care seems like something that you can’t do well if you don’t have a strong data strategy.

That’s one of the first things, because if you’re all of a sudden being measured on very specific criteria, you’re like, “How do I even know what to change to change the levers? How do I know what’s going to move the needle on which metrics?” This is definitely moving the needle. The CMS with the TPS, the Total Performance Scores, very specific scores for very specific things. What do I have to do to actually get a better score here and a better score there so that I can rank better so I can hopefully get better reimbursements. This has been a real forcing function to get more data-driven. I would say there’s always been a problem with churn, but the labor shortage, necessity is the mother of invention.

Because we have this labor shortage, that has been a real forcing function also to try and be more efficient, be more optimized, use these precious resources in a more optimal fashion, and make sure that they’re happy. I think that has really pushed the needle on needing to improve the technology.

I know we’ve talked in the past too, just about the shrinking margins that a lot of agencies are facing. You need the data component, the AI component, potentially the automation component, just to do more with less.

You have to eliminate as many repetitive tasks. I joined this industry about four and a half years ago, where I was meeting a lot of people who were still delighted because they came from a paper-based to a digital environment, and now we’re really going from digital to really data-driven. It’s a short history of really diving into this. Starting to use AI models, that’s really quite an accelerated path.

Now I feel like I’m talking to data teams and providers are hiring data scientists and they’re hiring data engineers, and they’re building these sophisticated data stacks because they’re like, “We’ve got to try and be more efficient and we’ve got to try and find the efficiency.” I’m really seeing a big change in the industry to try and be more data-driven and then leverage technology at every step of the way.

At the start of this conversation, you mentioned how you’re excited about sensors and wearables too, because they’re less gimmicky. What’d you mean by that?

I remember pre-COVID when people used to come to your office and knock on the door and show you stuff. People would come with all kinds of crazy stuff. Like, here, here’s a laser. You could point at your grandmother’s forehead, you get all of the vitals. Here’s another thing that you can stick under their shoe and you get all kinds of stuff. Some of them work some of the time, but if you have a sensor that has to be in their shoe to be able to tell if they’re at a risk of falling, somebody’s got to be there when that shoe doesn’t fit on properly or the sensor slips out, like it’s not as practical.

I find that the technology has gotten more sophisticated and it’s gotten less invasive. More like sensors in the home as opposed to cameras in every room, which you can’t do from a privacy perspective. You don’t want to have full cameras all the time. I think all of that. The other thing is that we’re all getting used to it.

You start to integrate that with how can that also care for the people that are in the home? Here’s a reminder, did you take this medicine, and with vision, you can see did they take the right medicine? I don’t think that’s the right one to take at this time. You can imagine all these scenarios and all that technology is quite mature. It’s ready to be used. It just has to be designed in a way that is seamless to how somebody lives.

I think that’s pretty exciting with the voice, the computer vision, the sensors, and then the newer older generation they’re more used to using technology. They know how to use Facebook and all that stuff. It’s not going to be as crazy a leap as maybe the generation right now to be able to start trusting and living with that technology.

Looking ahead, what tips and strategies could you provide to help home-based care agencies prepare for the future using a crawl, walk, run approach based on their current level of data utilization?

It’s at the beginning, where are you today? You need inventory of your data stack, your collection processes that you have right now. Really identify what are the main questions? What do I want the data to tell me? Come up with those hypotheses. What are the problem areas? We have really seen a reduction in profitability in this particular area. We have too many people, we have too many schedulers. What do we do about that? How do we reduce that?

You have to really pinpoint everything. Where is it very inefficient? Let’s see if we can pinpoint how to solve that. Find out what are the problems first, what’s the data that’s going to answer that? Then you can start getting sophisticated with putting processes into place, leveraging this sophisticated technology.

To learn more about how AlayaCare can help your organization ensure operations are consistent across multiple locations with real-time information updates for key stakeholders, visit https://www.alayacare.com/.

The post HHCN FUTURE: The Role of Data, AI and Emerging Technologies in Home Care appeared first on Home Health Care News.

Geoff Abraskin has been named the new president of the hospice division at Amedisys (Nasdaq: AMED). 

He has been with the company for nearly 15 years.

“Geoffrey Abraskin has been promoted to President of the Hospice Division!” Amedisys wrote in a LinkedIn post last week. “Geoff is a doctor of physical therapy and a certified wound specialist who joined Amedisys 14 years ago as an administrator and rehabilitation specialty director. He most recently served as SVP of the home health northeast region, and brings strong operational, clinical, integration, engagement and overall strategy expertise to the hospice team.”

Based in Baton Rouge, Louisiana, Amedisys is a provider of home health, hospice and high-acuity care services in the home. It has about 16,500 employees that deliver care through over 520 care centers in 37 states and the District of Columbia.

Abraskin recently sat down with Home Health Care News to chat about some of Amedisys’ recent successes.

“We’re seeing some green shoots from people applying for jobs,” he said. “We actually just had our best two months in terms of fills. We’re seeing contractor costs coming down, so that’s a big positive for us. But overall, there’s so many people that need home health today, and there’s just X number of capacity. We’re hiring more people. We just can’t hire fast enough.”

UnitedHealth Group’s (NYSE: UNH) Optum agreed to acquire Amedisys earlier this year.

Elevance Health’s Next CFO

Earlier this month, Elevance Health (NYSE: ELV) announced that CFO John Gallina would be retiring from his role as executive vice president and CFO later this year. In tandem with that news, the company announced that Mark Kaye would be taking over those positions.

Kaye was formerly the CFO at Moody’s Corporation. He will report directly to Gail Boudreaux, the president and CEO of Elevance Health. From September 6 to November 1, Kaye will serve as CFO Designate. After that, he will officially become the executive vice president and CFO of Elevance Health.

Gallina will also remain with the company as a special advisor to the CEO following his retirement. He has been at Elevance Health for nearly three decades, and was named CFO in 2016.

“On behalf of the entire Elevance Health team, I want to thank John for his contributions to our company over the last three decades,” Boudreaux said in a statement. “John has been a valued member of our organization who has successfully led our finance organization, navigated an ever-changing and dynamic health care landscape, and played an important role in our transformation to become a lifetime trusted health partner.”

Based in Indianapolis, Elevance Health is a large managed care company. It also is part provider, however, and has made in-home care a priority.

It most recently teased big plans to get more involved with at-home acute care.

“Mark is a well-respected leader with an extensive global finance background, who has significant experience in leveraging data-driven financial insight to support the execution of superior operational and strategic decisions, including growing and scaling businesses to drive success,” Boudreaux said in a statement. “With an innovative and customer first mindset coupled with his passion for fostering a high- performance culture, Mark will be a tremendous asset as we work to deliver on our purpose to improve the health of humanity.”

The Helper Bees’ Daniel Murphy joins the Family Caregiving Advisory Council

The Helper Bees announced last week that Daniel Murphy will join the federal Family Caregiving Advisory Council, which “makes recommendations to the administrator of ACL/Assistant Secretary for Aging on how to support and improve the lives of family caregivers.”

Murphy is the general manager of SaaS solutions at The Helper Bees. He previously was the co-founder and chief product officer of healthAlign, which was acquired by The Helper Bees in 2021. Before that, he served as national director of population health product and strategy for Maxim, a home health care provider.

“My experiences in the military led me to the home health care industry when I transitioned to the private sector, where I continue to serve others,” Murphy said in a statement. “I’ve spent the past decade in the aging services industry, helping families, insurance companies and policymakers deliver better care for veterans and older Americans who deserve quality care. I look forward to working with other members of the Council, advising on solutions to improve caregiving.”

Based in Austin, Texas, the Helper Bees is an insurtech company that partners with payers and home-based care providers to help coordinate care in the home.

Murphy will serve on the Family Advisory Council through 2026.

DUOS appoints chief strategy and growth officer

The home-based care startup DUOS has named Jenn Kerfoot as chief strategy and growth officer.

Kerfoot previously served as the chief experience officer at FarmboxRx.

“Coming off a stellar Series A funding close earlier this year, DUOS brings on Kerfoot at a critical time of expansion to lead all things growth, including business development, B2B sales, brand marketing and more,” the company said in a statement.

Indeed, DUOS announced in June that it raised $10 million, bringing its funding total to over $33 million. The round was led by Primetime Partners and SJF Ventures.

Based in New York, the company places personal care assistants into seniors’ homes. It also has proprietary software, which has become a larger part of its business model.

DUOS hired Tom Shankle as the company’s new VP of health solutions earlier this month.

AlayaCare brings on new SVP of customer success

AlayaCare announced that Rhonda Bosch is joining its executive leadership team as SVP of customer success.

In the her role, she will “spearhead customer success strategies and initiatives, driving unparalleled value and satisfaction for clients across the home care sector,” according to the company.

“We are delighted to welcome Rhonda to our executive team,” AlayaCare Founder and CEO Adrian Schauer said in a statement. “Her proven expertise in fostering customer relationships and driving growth aligns seamlessly with our mission to provide innovative solutions that exceed our client’s expectations.”

Previously, Bosch served as the COO of Mercatus Technologies Inc.

“I am honored to join AlayaCare and lead the customer success efforts,” Bosch said in a statement. “What drew me to AlayaCare is that our people really care. And when you care about making a difference, you really own it. AlayaCare works hard to make it easier for caregivers to be able to execute a high level of care, and I’m excited to help nurture and foster their customers to achieve better outcomes.”

The post Amedisys Finds New Hospice President From Within; Elevance Health Announces Next CFO appeared first on Home Health Care News.

Fortis Home Health & Hospice has named Rob Radics as the company’s new CEO.

Radics joins Fortis with nearly three decades of experience leading and operating home health and hospice businesses. Most recently, he served as the president of the home health and hospice segments at Aveanna Healthcare Holdings (Nasdaq: AVAH).

Fortis offers home health, hospice, palliative and personal care services in Utah and Indiana. The company is backed by the private equity firm Grant Avenue Capital.

“We are thrilled to welcome Rob to the Fortis team at this important point in the company’s evolution,” Buddy Gumina, founder and managing partner of Grant Avenue, said in a statement. “His extensive home-based care background, commitment to clinical excellence and proven leadership will help advance Fortis’ mission of building a leading home-based care company that is renowned for its outstanding patient care and dedicated workforce.”

Under new leadership, Fortis plans to accelerate its expansion plans, which include strategic acquisitions, partnerships and de novos.

“Grant Avenue has strategically cemented Fortis’ position as a high-integrity, experienced provider of choice for patients and families and as a partner of choice for our talented Fortis team,” Radics said in a statement. “I am incredibly proud to join the company as it embarks on its next phase of growth and advancement.”

Androscoggin appoints new chief clinical officer

Androscoggin Home Healthcare & Hospice has named Carol Weir as its new chief clinical officer. Weir has officially been at her new post since June 26.

“Carol is an experienced health care executive whose heart has never left the patient’s home or bedside,” Androscoggin President and CEO Ken Albert said in a statement. “She is known as a visionary leader who uses data and creative innovation to solve the challenges facing health care providers. I also find her to be a warm, respectful and kind person with a great sense of humor: a winning combination.”

The Maine-based Androscoggin is a nonprofit operator that employs 500 workers across all 16 counties in the state.

It has been steadily growing in recent years, most notably with the purchase of the home and behavioral health care company Care & Comfort.

Reimagine Care names new CEO

Reimagine Care has named Dan Nardi as the company’s new CEO.

Reimagine Care is a Nashville-based in-home cancer care enabler that provides tech services to health systems and oncologists to deliver home-centered, value-based cancer care.

The company believes the future of cancer care exists outside of the traditional facility setting.

Some of its key partners include the University of Colorado and City of Hope.

“We will continue to shift health care to financially reward providers and health care systems for delivering care with good health outcomes and experiences for the patients,” Devin Carty, chairman of the Reimagine Care board of directors, said in a statement. “Reimagine Care is a proven leader in partnering with independent oncology practices and healthcare systems to successfully shift towards sustainable, value-based care structures. Dan brings incredible operational and product experience and is a proven leader in the health care industry.”

Nardi was previously COO for Carrum Health.

Former CEO and co-founder Aaron Gerber will continue to support the organization as a senior advisor, the company announced.

Caring Senior Service hires new director of marketing

Caring Senior Service has named Devin Bevis as its new marketing director.

Caring Senior Service is a San Antonio-based home care provider with over 50 locations throughout the U.S.

“It’s a pleasure to be returning to the home care industry,” Bevis said in a statement. “I love working at the intersection of home care and franchising. One of the best things about marketing for a growing organization is the opportunity to collaborate with driven entrepreneurs to build meaningful relationships that contribute to the development of an even stronger franchise brand.”

Before joining Caring Senior Service, Bevis served as the executive director of franchising services for FirstLight Home Care.

“Devin is a fantastic addition to our leadership team,” Salter said in a statement. “We look forward to having Devin’s talents in both marketing and in the home care industry on hand to help us continue building our brand as a compassionate advocate for at-home care.”

AlayaCare names new chief revenue officer

Home-based care technology company AlayaCare announced that Chris Hare has joined the company’s executive leadership team as chief revenue officer.

“It’s not a secret that the home care industry is ripe for disruption with so much care transitioning into the home,” Hare said in a statement. “AlayaCare’s distinct opportunity is to lead that charge in being the partner of choice in North America and globally. I’m excited to build on AlayaCare’s foundation of improving patient outcomes and enhancing the satisfaction of staff and caregivers to accelerate growth in the U.S. market.”

Before joining AlayaCare, Hare served as the EVP of sales at Net Health, a health care technology organization.

“Chris Hare brings robust domain focus, experience, and fresh perspective to our executive leadership team, helping to position AlayaCare and its customers for our next phase of growth, especially in the US market,” AlayaCare founder and CEO said in a statement. “Chris will continue to use product-led growth strategies that provide customers with a first-hand experience of the true value of home care agency performance optimization. I am thrilled to have this strong leader on our executive bench.”

Maxwell Healthcare Associates names new COO

Maxwell Healthcare Associates (MHA) has announced Jay Duty will be the company’s new chief operating officer.

Before joining MHA, Duty served as the chief development officer at Enhabit Inc. (NYSE: EHAB).

Maxwell Healthcare Associates is a Minnesota-based post-acute consulting firm. As the company’s COO, Duty will oversee daily operations and be in charge of growth initiatives.

The post Fortis Home Health & Hospice’s Next CEO; Androscoggin Names New Chief Clinical Officer appeared first on Home Health Care News.

Home Health Care News: Some of the themes that we’ve talked about are the deal-making due diligence process and how it’s so difficult. Buyers are a little bit more timid to make big investments. In the past, they would maybe kick the tires on the car that they were trying to buy. Right now, they are taking it apart, scrubbing it down with a toothbrush. They are looking at things like software. I guess starting at the very beginning of all this, remind us why acquirers and capital providers here should consider agency management software when evaluating M&A opportunities.

Geoff Darling: I think for us, all the financial benefits that you anticipate when pursuing an acquisition or the return on investment, you are targeting the critical path to realizing that is flowing through the integration of those business practices.

When bringing two disparate business lines together—the company and the software—you need to have a strategy to bring those together, so we feel when you’re pursuing a deal thesis and looking at the geographic expansion, like the service lines and the addressable markets that are going to evolve as you first pursue that expansion, whether it’s your first big deal or the first of a series of deals you’re going to do, you need to have a playbook developed for how you’ll bring those target systems together with the system that you’re starting from.

HHCN: Ian, any thoughts you want to add there?

Ian Gordon: Yes, three points.

One: Whether you are doing an initial integration or a merger acquisition, you’re doing an implementation. When you do that, what you can learn from the due diligence process is that the implementation gives you your roadmap to so many different things. One of the things that I have done in the past is to look at every decision point that has to be made when you’re implementing a system. That gives you an idea of how your policies, procedures, and affairs are set up, so you truly dig in and understand your operating model.

Two: The second reason that’s profoundly important, is that when you think about your frontline folks, your clinicians, care givers and support staff, this system is their security blanket. Whether you’re doing a new implementation or trying to integrate two different organizations, you’re messing with their security blanket, and you really must understand what you are doing and positively engage them.

Three: In accord with Geoff’s point—all revenue comes through the system. With revenue cycle management (RCM) you must truly understand its critical role because I’ve seen so many organizations fail at that point because they’ve made inappropriate decisions or didn’t understand the implications because there’s high-level due diligence assumptions made. I think understanding that system is just core to everything.

George Psiharis: Yes, and I would add—building off everything that both Ian and Geoff have said so far—is basically couching this in context. Underlying each acquisition’s needs is a due diligence process as a business case with some kind of hypothesis around industrial logic. There’s some reason to put companies together, and with it in terms of evaluating how likely these hypotheses become reality and how fast. We have well-versed playbooks on financial due diligence and understanding all of that very deeply.

We have more emerging playbooks on the business’s operating system and operating due diligence and making sure that that is far enough along that we’ve removed risk from that side of things and are expediting this process to the outcomes that we want. The technology platform is often the biggest part of that. There’s no guarantee as well that it always comes from the bigger entity. It may be part of an acquisition strategy that’s taking place.

I think that across industry sectors, not just in healthcare and in home health, we’re seeing that evaluating the operating systems with the business in more depth and with the technology infrastructure that underlies that as being important. Of course, it is part of the integration as well, the data architecture underlying operating systems. If you’re using tools that are of high quality, you’re more likely to have a data situation that’s not a mess and can be more readily ETL or sensor extract, transform, load into an integrated set of properties that will go much more smoothly and get to those outcomes much better.

Then, on the flip side, I would add a great way for someone looking to get acquired to show readiness is to make these investments early and to have a lot of that work taken care of.

HHCN: We just teed up the conversation on why software architecture is so critical for acquirers. That concept holds true, not just for the platform you might be buying into, but potential follow-on acquisitions. Digging into this though, what specific things do you want to look at for software architecture? What characteristics could help buyers fuel efficient, scalable, and repeatable M&A?

Gordon: For me, one of the most important things will be extensibility. If you start looking at a platform, if you designed starting today, by the time you deliver it, it is out of date, you’re behind, you’re trying to catch up. It is important that you see how the future—which is unknown in mind—obviously will meet the current needs and be extensible to the future.

Gordon: I think when you move past that, obviously with flexibility, because the market and the model changes. When you look at healthcare as a broader industry it’s just evolving at a pace that’s unbelievable. Home health is really moving at a quick pace. A great example is value-based arrangements.

As you look at the platforms that people have in place, some of them are not extensible, not flexible enough to handle value-based arrangements, so I think that’s critical.

The next piece I would say is that there’s an openness to the environment. You’ve got some closed platforms out there. Then, I’ve been involved in organizations where we’ve had our core platform as a vendor platform. We’d go and ask questions for something that we needed, and they would say, “Sure we can do that. We’ll put it on the roadmap.” I said, “Great, so when should we expect it, 2027?” Those types of things just don’t help. If it’s closed, the challenge that we had was we couldn’t go through the platform. We couldn’t use that. We had to find ways to work around it which create tremendous amounts of inefficiencies and cost to your system. That’s really, from my perspective, a pretty bad thing.

The last piece I would talk about is the data side of things. As you’re looking at the data environment you need to be able to extract your data from an enterprise-grade environment.

You need to be able to get the data out, get data in, be able to share it, not just across your environment, your own environment, and operate it, but the entire ecosystem, not just technically, but business process perspective, reporting, analytics, and all types of things. It would be those three things really. Extensibility, flexibility, openness, and then the ability to handle the data.

HHCN: On the sharing point, I imagine, share it in a way that makes sense to those parties that are receiving the data.

Gordon: Yes, it needs to be obviously secure, but an openness to how you’re making data available to others and that is probably the most critical piece. As we heard earlier, when you talk about trying to create integrated care models, it is difficult to create an integrated care model when you’re trying to have three partners who are outside of your true technical environment to leverage data.

What happens is you put the patient in the middle of what’s going on, not at the center of what’s going on, which is where they’re supposed to be, but in the middle, because we’ve now made our data sharing problems an issue for them having to repeat themselves not knowing that something’s already been done, those types of things. That’s why it is so critical.

HHCN: George, any other characteristics or factors that acquirers really need to consider in that software architecture?

Psiharis: I think it must be very closely aligned—but also different in some nuanced ways from what Ian talked about—is configurability. One of my favorite themes that I’ve seen come up over the course of the day so far, I’m really thrilled to see come up, it hearkens to a Peter Drucker quote is, “Culture eats strategy for breakfast.”

I think the tendency is to try to get into standardized, use these operating systems as perhaps somewhat rigid approaches to guarantee a level of quality, but also sustainable financial performance across multiple businesses, across multiple service lines even that can work up to a point. I think the reason most acquisitions fail, from an operating perspective, is that the culture rejects the processes that are being put in place. If a platform can be configurable, it can be standardized at a high level, but at a micro level, it can allow some of those nuances that allow culture to adapt to whatever system you’re trying to get folks to use.

I think that could be a lot more powerful if done well in de-risking but also creating opportunities for success. To make it a bit more specific, on the external facing side, what are the little things a team can do to use a common operating system and tech tool, but to add little brand touches or to customize the client experience in little ways that really matter to them and make them feel like they’ve preserved a bit autonomy in the way that they provide care?

On the internal side, what are the ways that they can plug into doing that, but also meeting all the internal requirements and some of the benefits that come from best practices, short playbooks, and standardized datasets, especially in sources of truth in reporting? That’s the one thing I would add. I’ll leave it at that.

HHCN: The two headline items I think we’ve gotten so far, extensibility, configurability. Could you hit me with another one?

Darling: When I think about acquisition strategies, I don’t think it’s Peter Drucker, “Indigestion kills more companies than appetite.” My answer is more of a riff off yours, which is centralizing your business intelligence. You need an operating system that allows you at HQ to understand what’s going on in your region, what’s going on in your branches, at the same time, depending on your operating model, you need to be able to deploy responsibility for making the right frontline decisions in your organization.

That’s something that we found when we were dealing in our software worlds with franchise models versus large national operators, there’s always differences in philosophies on how much regional versus HQ decision-making they want to be able to do. I think that’s fundamental in terms of how you approach that software architecture for that platform as you’re building it out, or as you’re adding to it, make sure everything’s feeding back the right way so that you can have empowered decision-making across.

HHCN: You mentioned indigestion. Ian, were you about to say something?

Gordon: I’d just like to add something there because it ties so much into what these two gentlemen were saying, is that when you’re doing an integration, it’s a great time to not do it in a vacuum, to engage the people who are there. You can think about it. All too often folks think that the acquirer or the bigger entity if it’s a merger of close to equals has the right answers. It’s a perfect opportunity to engage the frontline in grades, do organization, look at things, and purposefully and very thoughtfully make decisions about moving forward. Everybody wanted the synergies yesterday. Today’s okay but tomorrow is too late.

From my perspective, you can pay us now or you can pay us later because you are going to end up having to deal with the cultural issues that you talked about, the reporting issues that you talked about. The last thing we all know that we can afford to do is alienate the front line.

HHCN: You went where I wanted to go with the next question. If we are even putting software aside for a second, what are one or two things that really need to go well to fuel a successful integration process? What are the things acquirers should really be laser-focused on?

Psiharis: Yes, there is still great value in spending time on the change leadership aspect and over-communicating the why behind decisions that are made. When we’re going through processes of picking best practices, standardizing them across portfolios, if there isn’t a healthy amount of why behind those decisions that are being made, transparency and visibility into that, and almost over-communication, and a closed loop process of circling back to these decisions, I think that can be really, really damaging. That’s where, again, the culture side of things starts to drift away from the benefits that we know can happen in these types of scenarios.

I think also having very clear objectives that everybody is aware of and being able to remind folks that this is a part of the why. You need to change the process for standardizing this across the portfolio that can seem very tone-deaf to a frontline team, to folks providing care. We’re doing it because the pros significantly outweigh the cons, but we’re going to be real about the fact that there are cons as well and effort and lift that needs to go into some of these change decisions.

Those things are hard to do, but crucial. That’s a place where I think you can never spend too much time really supporting major changes to operating models and with it, introducing or adapting technology platforms that are part of that.

Darling: I would also emphasize post-merger integration. I feel like leading into the deal, you’re going to have a very detailed plan and it’s going to be exactly wrong. Ensuring that you don’t lean in too hard and stay too rigid in how that plan gets deployed through the process because there will be hiccups. I think part of that PMI planning is having clear accountabilities. You’ve got vendor partners, you’ve got target and acquire change management elements, and you’ve likely got third-party consultants, depending on the scale of your organization involved.

Knowing who’s responsible for what aspects of a migration is incredibly important. Everybody loves quick wins, whether in any part of their job, but I think there’s a danger in rushing to claim some quick wins in terms of realizing synergies with an acquisition that backfires. Instead of paying once to do it right, you pay twice to fix it. A related note is moving too quickly on the people’s side. Where there may be synergies and HQ and administrative layers, we often see a tendency to rush those before the processes are done and fully cooked. Those, again, will come back and bite you in like, “You know what?”

HHCN: Ian, did you have anything else that you wanted to add?

Gordon: I think I need to leave you with the visual on what Geoff was talking about, is that you must ask yourself in this integration process and your planning process, are you Wile E. Coyote? Are you the Road Runner? If you remember back to your cartoon days, the Wile E. Coyote always had that detailed plan, followed it, and always lost, and got crushed by a rock, whereas the Road Runner had that plan. It wasn’t perfectly wrong. It was directionally correct and remained flexible throughout time. You can never think that doing something as large as a system integration or implementation, that you actually have the right plan.

To learn more about how AlayaCare can help your organization ensure operations are consistent across multiple locations with real-time information updates for key stakeholders, visit https://www.alayacare.com/.

The post HHCN Capital + Strategy: Capital + Consolidation Needs More Software to = Growth appeared first on Home Health Care News.

Top home-based care leaders from across the country gathered at the end of March in Washington, D.C. We got to hear from many of them at Home Health Care News’ Capital + Strategy event last Thursday.

While they are advocating for a better regulatory environment – whether in personal home care or home health care – almost all of them are now operating with an understanding of the hand they’ve been dealt.

The headwinds are strong, but the value proposition, these leaders believe, is stronger.

Providers are no longer holding a sign that says, “We’re valuable.” They are handing out pamphlets with detailed bullet points on why they are valuable and how that should be recognized in the payer community, whether by the Centers for Medicare & Medicaid Services (CMS) or Medicare Advantage (MA) plans.

“There’s been a mindset that’s changed over the last couple of years,” Daniel Schwartz, chief strategy officer for Elara Caring, said at the event. “A couple of years ago, the conversation was that we had a battle to be fought and an enemy to attack. Now, … there’s a recognition that when home care is properly deployed, it adds value for plans’ members. That was not always a prevalent mindset.”

All the while, they are looking to drive efficiencies throughout their operations. The staffing crisis is no longer cause for woe-is-me rhetoric, but instead, an issue that they are finding ways through.

“We will never have enough caregivers – ever,” AccordCare CEO Brandon Ballew also said at the event. “Even if we passed a handful of bills and got people away from flipping burgers or [other areas], we just don’t have enough.”

MA enrollment and job vacancies remain on the rise.

Thus, the successful home-based care companies will find ways to prove their value to payer sources while driving efficiencies in 2023 and beyond.

How they’re going to do that is the topic of this week’s members-only, exclusive HHCN+ Update.

How to make do

Given Ballew’s assertion that meeting the worker demand in home-based care is virtually impossible, companies will have to augment their workforces.

They’ll have to do that through stretching out those workers based on their abilities and time, and also through technology.

“I think there are technology improvements that are critical,” Ballew said. “We’ve now seen technology partners that were outside of the home care industry start to get interested in home care. They’re starting to apply products that they had in other areas, saying, ‘Hey, I think that might work in home care.’ We’re recognizing that we can possibly reduce some hours, maybe reduce some visits, because we’ve got technical, logical connections, and we can be smarter about care interventions in the home by using that technology.”

The Atlanta-based AccordCare is a provider of both personal home care and home health care services in eight states: New York, Connecticut, New Jersey, Georgia, South Carolina, North Carolina, Alabama and Florida.

Ballew also believes that Accordcare can leverage all its workers’ skills, creating a more flexible workforce in the meantime.

“Push the envelope on what you can do with their training and licensure,” he continued. “Don’t go outside of it; I’m not saying do that. But I need that aide to do as much as they possibly can. And that’s likely more than they’ve done in the past. We hope we can get them to do more in the future. And that goes for LPNs, PTs – up and down the board.”

On the home health side, clinicians are already having to see more patients than before, but performing less visits per patient.

“Overall, the agencies have done a great job in figuring out where to apply those two less visits per episode,” Scott Pattillo, chief strategy officer for the technology platform Homecare Homebase, said at the event. “And, amazingly enough, we’ve not seen [an uptick] there in rehospitalizations.”

Technology and dealmaking

A burgeoning area like home-based care is bound to be disrupted by technology. But disruption is not necessarily a bad thing. In fact, it can be what keeps providers’ heads above water.

It’s also becoming an increasingly important part of merging two businesses after a deal.

“When bringing two disparate business lines together, you need to have a strategy to bring the software together,” Geoff Darling, the SVP of corporate development and strategy at the home care software company AlayaCare, said at the event. “Whether it’s your first big deal or the first of a series of deals you’re going to do, you need to have a playbook developed for how you bring those target systems together.”

On that note, valuations are not coming down – especially in the low- to mid-sized market. Despite the fact that providers are not getting what they want from policymakers in D.C., sellers are still getting what they want, monetarily, in deals.

“The lower end of the market is holding up fine,” Mertz Taggart Managing Partner Cory Mertz told HHCN. “I can make the case that values for companies in this size range are higher today than before interest rates started to rise.”

Fair pricing for value

Rates for services have always been a point of contention between CMS and home health providers. The same can be said for MA plans and home-based care providers generally.

Part of that is just based on how plans work.

“I think what makes it difficult is understanding how large organizations like health insurance companies work,” Joy Cameron, the associate vice president of public policy at Humana Inc. (NYSE: HUM), said at Capital + Strategy. “Honestly, it’s a lot about capacity and coming together and talking about how many patients [a provider can take in]. If you’re going to work hard and get into a value-based relationship, let’s make sure you have the capacity to take these patients and that every time one gets referred to you, it’s not getting pushed off.”

Plans are becoming more well-informed on how home-based care providers operate.

At the same time, providers are starting to understand that they need to tell providers what they can do to help them, and not just demand more pay.

“So, how can we improve that relationship?” Cameron continued. “How can we improve that back and forth? How much can we automate when it comes to prior authorization and things like that? We’ve done a lot to really improve the way that works on our end, especially with our known providers, to just make it a lot smoother.”

Those relationships are improving, leaders suggest. But in the areas where they’re not, those leaders are taking note.

“We are payer agnostic, but we’re payer selective,” Traditions Health CEO David Klementz also said.

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